Below are some examples of coding projects I completed during a Fall 2024 course in election-related data analysis at MTSU.

GRAPI (Gross Rent as as Percentage of Income) by Tennessee House District:

Financial experts recommend spending no more than 30% of a household’s income on housing. The map below shows the percentage of households overspending on housing.

District	Estimate	Estimate_MOE	From	To
Estimate by district
State House District 13 (2022), Tennessee	42.9	7.7	35.2	50.6
State House District 37 (2022), Tennessee	40.3	5.5	34.8	45.8
State House District 34 (2022), Tennessee	35.9	4.8	31.1	40.7
State House District 49 (2022), Tennessee	35.7	5.6	30.1	41.3
State House District 48 (2022), Tennessee	34.4	5.9	28.5	40.3

R Code:

# Installing and loading required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("tidycensus"))
  install.packages("tidycensus")
if (!require("sf"))
  install.packages("sf")
if (!require("mapview"))
  install.packages("mapview")
if (!require("gtExtras"))
  install.packages("gtExtras")

library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(gtExtras)

# Transmitting API key

census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")

# Fetching ACS codebooks

DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
Codebook <- DetailedTables %>% 
  select(name, label, concept)
Codebook <- bind_rows(Codebook,SubjectTables)
Codebook <- bind_rows(Codebook,ProfileTables)
Codebook <- Codebook %>% 
  distinct(label, .keep_all = TRUE)
rm(DetailedTables,
   SubjectTables,
   ProfileTables)

# Filtering the codebook

MyVars <- Codebook %>% 
  filter(grepl("GRAPI", label) &
           grepl("Percent!!", label))

# Making a table of the filtered variables

MyVarsTable <- gt(MyVars) %>%
  tab_header("Variables") %>%
  cols_align(align = "left") %>%
  gt_theme_538

# Displaying the table

MyVarsTable

# Defining the variable to retrieve

VariableList = 
  c(Estimate_ = "DP04_0142P")

# Fetching data

AllData <- get_acs(
  geography = "state legislative district (lower chamber)",
  state = "TN",
  variables = VariableList,
  year = 2022,
  survey = "acs5",
  output = "wide",
  geometry = TRUE
)

# Mutating, selecting and sorting the data

AllData <- AllData %>%
  mutate(
    District = NAME,
    Estimate = Estimate_E,
    Estimate_MOE = Estimate_M,
    From = round(Estimate - Estimate_MOE, 2),
    To = round(Estimate + Estimate_MOE, 2)
  ) %>%
  select(District, Estimate, Estimate_MOE, From, To, geometry) %>%
  arrange(desc(Estimate))

# Filtering for Rutherford County districts

MyData <- AllData %>%
  filter(
    District == "State House District 13 (2022), Tennessee" |
      District == "State House District 37 (2022), Tennessee" |
      District == "State House District 49 (2022), Tennessee" |
      District == "State House District 48 (2022), Tennessee" |
      District == "State House District 34 (2022), Tennessee"
  )

# Producing a map

MapData <- st_as_sf(MyData)

MyMap <- mapview(MapData,
        zcol = "Estimate",
        layer.name = "Estimate",
        popup = TRUE)
#Displaying the map

MyMap

# Producing a table

TableData <- st_drop_geometry(MapData)
MyTable <- gt(TableData) %>%
  tab_header("Estimate by district") %>%
  cols_align(align = "left") %>%
  gt_theme_538

# Displaying the table

MyTable

Precinct-level Rutherford Election Results From 2022

(Race for Governor, 2022)

Below is a map showing the percentages of voters in favor of Bill Lee in the 2022 election.

# Required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("mapview"))
  install.packages("mapview")
if (!require("sf"))
  install.packages("sf")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")
if (!require("plotly"))
  install.packages("plotly")

library(tidyverse)
library(mapview)
library(sf)
library(leaflet)
library(leafpop)
library(readxl)
library(plotly)

# Download and import election data
# from TN Secretary of State web site:
# https://sos.tn.gov/elections/results

download.file(
  "https://sos-prod.tnsosgovfiles.com/s3fs-public/document/20221108AllbyPrecinct.xlsx",
  "RawElectionData.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData <- read_xlsx("RawElectionData.xlsx", sheet = "SOFFICELso")

# Filter, calculate, and select
# to get data of interest
# then store results in MyData dataframe

MyData <- RawElectionData %>%
  filter(COUNTY == "Rutherford", CANDGROUP == "1") %>%
  mutate(
    Lee = PVTALLY1,
    Martin = PVTALLY2,
    Total = PVTALLY1 + PVTALLY2,
    Lee_Pct = round(PVTALLY1 / (PVTALLY1 + PVTALLY2), 2),
    Martin_Pct = round(PVTALLY2 / (PVTALLY1 + PVTALLY2), 2),
    Winner = case_when(
      PVTALLY1 > PVTALLY2 ~ "Lee (R)",
      PVTALLY2 > PVTALLY1 ~ "Martin (D)",
      .default = "Tie"
    )
  ) %>%
  select(COUNTY, PRECINCT, Total, Lee, Martin, Lee_Pct, Martin_Pct, Winner)

# Download and unzip a precinct map to pair with the vote data

download.file("https://github.com/drkblake/Data/raw/main/Voting_Precincts_5_31_24.zip","TNVotingPrecincts.zip")

unzip("TNVotingPrecincts.zip")

All_Precincts <- read_sf("Voting_Precincts_5_31_24.shp")

# Filter for particular county precincts

County_Precincts <- All_Precincts %>%
  filter(COUNTY == 149) %>%
  rename(PRECINCT = NEWVOTINGP)

# Merge election data and map file

MergeFile <- merge(MyData, County_Precincts, by = "PRECINCT", all.x = TRUE)

# Drop unneeded columns from MergeFile

MergeFile <- MergeFile %>%
  select(PRECINCT,
         Total,
         Lee,
         Martin,
         Lee_Pct,
         Martin_Pct,
         Winner,
         geometry)

# Format MergeFile as a map, and
# call the map MyMap

MyMapFile <- st_as_sf(MergeFile)

mypalette = colorRampPalette(c('blue', 'red'))

MyMap <- mapview(
  MyMapFile,
  zcol = "Lee_Pct",
  col.regions = mypalette, at = seq(0, 1, .2),
  map.types = ("OpenStreetMap"),
  layer.name = "Pct. for Lee",
  popup = popupTable(
    MyMapFile,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "PRECINCT",
      "Lee",
      "Martin",
      "Total",
      "Lee_Pct",
      "Martin_Pct",
      "Winner"
    )
  )
)

# Showing the map

MyMap

MyMap <- mapview(
  MyMapFile,
  zcol = "Lee_Pct",
  col.regions = mypalette, at = seq(0, 1, .2),
  map.types = ("OpenStreetMap"),
  layer.name = "Pct. for Lee",
  popup = popupTable(
    MyMapFile,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "PRECINCT",
      "Lee",
      "Martin",
      "Total",
      "Lee_Pct",
      "Martin_Pct",
      "Winner"
    )
  )
)

# Showing the map

MyMap

R Code

# Required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("mapview"))
  install.packages("mapview")
if (!require("sf"))
  install.packages("sf")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")
if (!require("plotly"))
  install.packages("plotly")

library(tidyverse)
library(mapview)
library(sf)
library(leaflet)
library(leafpop)
library(readxl)
library(plotly)

# Download and import election data
# from TN Secretary of State web site:
# https://sos.tn.gov/elections/results

download.file(
  "https://sos-prod.tnsosgovfiles.com/s3fs-public/document/20221108AllbyPrecinct.xlsx",
  "RawElectionData.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData <- read_xlsx("RawElectionData.xlsx", sheet = "SOFFICELso")

# Filter, calculate, and select
# to get data of interest
# then store results in MyData dataframe

MyData <- RawElectionData %>%
  filter(COUNTY == "Rutherford", CANDGROUP == "1") %>%
  mutate(
    Lee = PVTALLY1,
    Martin = PVTALLY2,
    Total = PVTALLY1 + PVTALLY2,
    Lee_Pct = round(PVTALLY1 / (PVTALLY1 + PVTALLY2), 2),
    Martin_Pct = round(PVTALLY2 / (PVTALLY1 + PVTALLY2), 2),
    Winner = case_when(
      PVTALLY1 > PVTALLY2 ~ "Lee (R)",
      PVTALLY2 > PVTALLY1 ~ "Martin (D)",
      .default = "Tie"
    )
  ) %>%
  select(COUNTY, PRECINCT, Total, Lee, Martin, Lee_Pct, Martin_Pct, Winner)

# Download and unzip a precinct map to pair with the vote data

download.file("https://github.com/drkblake/Data/raw/main/Voting_Precincts_5_31_24.zip","TNVotingPrecincts.zip")

unzip("TNVotingPrecincts.zip")

All_Precincts <- read_sf("Voting_Precincts_5_31_24.shp")

# Filter for particular county precincts

County_Precincts <- All_Precincts %>%
  filter(COUNTY == 149) %>%
  rename(PRECINCT = NEWVOTINGP)

# Merge election data and map file

MergeFile <- merge(MyData, County_Precincts, by = "PRECINCT", all.x = TRUE)

# Drop unneeded columns from MergeFile

MergeFile <- MergeFile %>%
  select(PRECINCT,
         Total,
         Lee,
         Martin,
         Lee_Pct,
         Martin_Pct,
         Winner,
         geometry)

# Format MergeFile as a map, and
# call the map MyMap

MyMapFile <- st_as_sf(MergeFile)

mypalette = colorRampPalette(c('blue', 'red'))

MyMap <- mapview(
  MyMapFile,
  zcol = "Lee_Pct",
  col.regions = mypalette, at = seq(0, 1, .2),
  map.types = ("OpenStreetMap"),
  layer.name = "Pct. for Lee",
  popup = popupTable(
    MyMapFile,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "PRECINCT",
      "Lee",
      "Martin",
      "Total",
      "Lee_Pct",
      "Martin_Pct",
      "Winner"
    )
  )
)

# Showing the map

MyMap

Early Voting Map

Below is a map of early voting locations in Rutherford County.

MTSU is marked in blue as a reference point.

R Code:

# Required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("sf"))
  install.packages("sf")
if (!require("mapview"))
  install.packages("mapview")
if (!require("leaflet")) 
  install.packages("leaflet")
if (!require("leaflet.extras2")) 
  install.packages("leaflet.extras2")

library(tidyverse)
library(sf)
library(mapview)
library(leaflet)
library(leaflet.extras2)
library(leafpop)

mapviewOptions(basemaps.color.shuffle = FALSE)

# Load the address and lat/long data

Addresses_gc <- read_csv("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/EarlyVotingLocations_gc.csv")

# Add MTSU

long <- -86.361861
lat <- 35.848997

Addresses_gc <- Addresses_gc %>% 
  add_row(Location = "MTSU",
          long = long,
          lat = lat) %>% 
  mutate(Point = case_when(Location == "MTSU" ~ "MTSU",
                           TRUE ~ "Early vote here"))

MapData <- st_as_sf(Addresses_gc,
                    coords = c("long", "lat"),
                    crs = 4326)

# Make the map

MyMap <- mapview(MapData,
                 zcol = "Point",
                 layer.name = "Point",
                 col.regions = c("orange", "blue"),
                 map.types = c("OpenStreetMap","Esri.WorldImagery"),
                 popup = popupTable(
                   MapData,
                   feature.id = FALSE,
                   row.numbers = FALSE,
                   zcol = c("Location",
                            "Address",
                            "Week",
                            "Weekend")))
# Show the map

MyMap

Early Vote Tracker

Below are day-by-day voting totals in Rutherford County.

Below is a precinct-divided map showing percentages of registered voters that have submitted their ballots thus far.

R Code

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("foreign"))
  install.packages("foreign")
if (!require("sf"))
  install.packages("sf")
if (!require("scales"))
  install.packages("scales")
if (!require("mapview"))
  install.packages("mapview")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")

library(tidyverse)
library(foreign)
library(sf)
library(scales)
library(mapview)
library(leaflet)
library(leafpop)

# Fetch and unzip the early voting files

download.file("https://github.com/drkblake/Data/raw/refs/heads/main/DailyEVFiles.zip","DailyEVFiles.zip")

unzip("DailyEVFiles.zip")

# Read the first daily voting file

AddData <- read.dbf("10162024.dbf")
AllData <- AddData

# Add each day's  file name to this list, then run

datafiles <- c("10172024.dbf",
               "10182024.dbf",
               "10192024.dbf",
               "10212024.dbf",
               "10222024.dbf",
               "10232024.dbf",
               "10242024.dbf",
               "10252024.dbf",
               "10262024.dbf",
               "10282024.dbf",
               "10292024.dbf",
               "10302024.dbf",
               "10312024.dbf")

for (x in datafiles) {
  AddData <- read.dbf(x, as.is = FALSE)
  AllData <- rbind(AllData, AddData)
}

# Save AllData file as .csv
write_csv(AllData,"EarlyVoterData2024.csv")

# Get total votes so far

TotalVotes <- nrow(AllData)
PctVotes <- round((TotalVotes / 224746)*100, digits = 0)

VotesByDay <- AllData %>% 
  group_by(VOTEDDATE) %>% 
  summarize(Votes = n()) %>% 
  rename(Date = VOTEDDATE) %>% 
  mutate(Date = (str_remove(Date,"2024-")))

# "#2C7865" is a green shade

chart = ggplot(data = VotesByDay,
               aes(x = Date,
                   y = Votes))+
  geom_bar(stat="identity", fill = "#41B3A2") +
  geom_text(aes(label=comma(Votes)),
            vjust=1.6,
            color="black",
            size=3.5)+
  theme(
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    panel.background = element_blank())

chart

### Precinct-level map of early voting turnout ***

# Aggregate early voting data by precinct

PrecinctData <- AllData %>% 
  group_by(PCT_NBR) %>% 
  summarize(Votes = n()) %>% 
  rename(Precinct = PCT_NBR)

# Download and unzip a precinct map to pair with the vote data

download.file("https://github.com/drkblake/Data/raw/main/Voting_Precincts_5_31_24.zip","TNVotingPrecincts.zip")

unzip("TNVotingPrecincts.zip")

All_Precincts <- read_sf("Voting_Precincts_5_31_24.shp")

# Filter for RuCo precincts and 
# strip dash from precinct numbers

County_Precincts <- All_Precincts %>%
  filter(COUNTY == 149) %>%
  rename(Precinct = NEWVOTINGP) %>% 
  mutate(Precinct = (str_remove(Precinct,"-")))

MapData <-  left_join(PrecinctData, County_Precincts, by = "Precinct")

RegData <- read_csv("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/RegVotersRuCo.csv") %>% 
  mutate(Precinct = as.character(Precinct))

MapData <- left_join(MapData, RegData, by = "Precinct")

MapData <- MapData %>% 
  mutate(Percent = round((Votes/RegVoters)*100), digits = 0) %>%
  rename(Voters = RegVoters) %>% 
  select(Precinct, Votes, Voters, Percent, geometry)

MapData_sf <- st_as_sf(MapData)

Map <- mapview(
  MapData_sf,
  zcol = "Percent",
  layer.name = "Pct. early voted",
  popup = popupTable(
    MapData_sf,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "Precinct",
      "Votes",
      "Voters",
      "Percent"
    )
  )
)

Map

MinTurnout <- min(MapData$Percent)
MaxTurnout <- max(MapData$Percent)
MedianTurnout <- median(MapData$Percent)
MeanTurnout <- mean(MapData$Percent)

Cable News Topic Tracker

This interactive plotly chart lets you compare the amount of cable news coverage mentioning “Donald Trump,” “Joe Biden,” and “Kamala Harris” between late April the most recent period for which data are available.

MSNBC:

CNN:

Fox News:

R Code:

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("plotly"))
  install.packages("plotly")
library(tidyverse)
library(plotly)

# Defining date range

startdate <- "20240429"
enddate <- "20241112"

### Trump

# Defining query

# Note:
# In queries, use %20 to indicate a space
# Example: "Donald%20Trump" is "Donald Trump"
# Use parentheses and %20OR%20 for "either/or" queries
# Example: "(Harris%20OR%20Walz)" is "(Harris OR Walz)"

query <- "Donald%20Trump"

# Building the volume dataframe

vp1 <- "https://api.gdeltproject.org/api/v2/tv/tv?query="
vp2 <- "%20market:%22National%22&mode=timelinevol&format=csv&datanorm=raw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Trump <- read_csv(v_url)
Trump <- Trump %>%
  rename(Date = 1, Trump = 3)

### Biden

# Defining query

query <- "Joe%20Biden"

# Building the volume dataframe

vp1 <- "https://api.gdeltproject.org/api/v2/tv/tv?query="
vp2 <- "%20market:%22National%22&mode=timelinevol&format=csv&datanorm=raw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Biden <- read_csv(v_url)
Biden <- Biden %>%
  rename(Date = 1, Biden = 3)

AllData <- left_join(Trump, Biden)

### Harris

# Defining query

query <- "Kamala%20Harris"

# Building the volume dataframe

vp1 <- "https://api.gdeltproject.org/api/v2/tv/tv?query="
vp2 <- "%20market:%22National%22&mode=timelinevol&format=csv&datanorm=raw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Harris <- read_csv(v_url)
Harris <- Harris %>%
  rename(Date = 1, Harris = 3)

AllData <- left_join(AllData, Harris)

### Graphic

AllData <- AllData %>%
  arrange(Date)

# Add "WeekOf" variable to the data frame

if (!require("lubridate"))
  install.packages("lubridate")
library(lubridate)

AllData$WeekOf <- round_date(AllData$Date,
                             unit = "week",
                             week_start = getOption("lubridate.week.start", 1))

CombinedCoverage <- AllData %>%
  group_by(WeekOf) %>%
  summarize(
    Trump = sum(Trump, na.rm = TRUE),
    Biden = sum(Biden, na.rm = TRUE),
    Harris = sum(Harris, na.rm = TRUE)
  )

fig <- plot_ly(
  CombinedCoverage,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
fig <- fig %>% add_trace(y = ~ Biden,
                         name = 'Biden',
                         fillcolor = '#507687')

fig <- fig %>% add_trace(y = ~ Harris,
                         name = 'Harris',
                         fillcolor = '#384B70')
fig <- fig %>% layout(
  title = 'Segment counts, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

fig

### Results for MSNBC, CNN, and Fox News, separately

# MSNBC

MSNBC <- AllData %>%
  filter(Series == "MSNBC")
figMSNBC <- plot_ly(
  MSNBC,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
figMSNBC <- figMSNBC %>% add_trace(y = ~ Biden,
                                   name = 'Biden',
                                   fillcolor = '#507687')
figMSNBC <- figMSNBC %>% add_trace(y = ~ Harris,
                                   name = 'Harris',
                                   fillcolor = '#384B70')
figMSNBC <- figMSNBC %>% layout(
  title = 'Segment counts, MSNBC, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

figMSNBC

# CNN

CNN <- AllData %>%
  filter(Series == "CNN")
figCNN <- plot_ly(
  CNN,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
figCNN <- figCNN %>% add_trace(y = ~ Biden,
                               name = 'Biden',
                               fillcolor = '#507687')
figCNN <- figCNN %>% add_trace(y = ~ Harris,
                               name = 'Harris',
                               fillcolor = '#384B70')
figCNN <- figCNN %>% layout(
  title = 'Segment counts, CNN, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

figCNN

#Fox News

FoxNews <- AllData %>%
  filter(Series == "FOXNEWS")
figFox <- plot_ly(
  FoxNews,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
figFox <- figFox %>% add_trace(y = ~ Biden,
                               name = 'Biden',
                               fillcolor = '#507687')
figFox <- figFox %>% add_trace(y = ~ Harris,
                               name = 'Harris',
                               fillcolor = '#384B70')
figFox <- figFox %>% layout(
  title = 'Segment counts, Fox News, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

figFox

Electoral Votes

2024 Presidential Election

Electoral Vote Map

Votes by Candidate

TN County Voting Shifts

Republican Donald Trump easily won Tennessee in both the 2020 and 2024 presidential races. But this analysis suggests he won in a different way each time.

You can explore the maps below to compare the two elections in terms of Republican and Democratic county-level gains and losses compared to the preceding presidential race. Specifically:

Drag a map’s slider left or right to see which counties produced fewer, more, or the same number of votes for each party compared to the most-recent presidential contest.
Click on a county to see vote counts for each election.
- Zoom and pan the map as needed. One possibly helpful tactic is to zoom in on a county, then position the slider so that you can look at the county’s Democratic and Republican vote totals simultaneously.
  
  Voting Shifts, 2016-2020:
  
  This map shows county-level vote shifts by party for the 2016 and 2020 elections. In 2016, Trump beat Democratic nominee Hillary Clinton. In 2020, Trump lost to Democratic nominee Joe Biden.

Voting shifts, 2020-2024

This map shows county-level vote shifts by party for the 2020 and 2024 elections. In 2020, Trump lost to Democratic nominee Joe Biden. In 2024, Trump beat to Democratic nominee Kamala Harris.

# Required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("mapview"))
  install.packages("mapview")
if (!require("sf"))
  install.packages("sf")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")
if (!require("plotly"))
  install.packages("plotly")
if (!require("tidycensus"))
  install.packages("tidycensus")

library(tidyverse)
library(mapview)
library(sf)
library(leaflet)
library(leafpop)
library(readxl)
library(plotly)
library(tidycensus)

# Go ahead and transmit your Census API key
# so you don't forget to do it later when getting
# the map you will need:

census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")

# Download and import election data
# from TN Secretary of State web site:
# https://sos.tn.gov/elections/results

# Get 2016 data

download.file(
  "https://sos-tn-gov-files.s3.amazonaws.com/StateGeneralbyPrecinctNov2016.xlsx",
  "RawElectionData2016.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData2016 <- read_xlsx("RawElectionData2016.xlsx")

# Filter, calculate, and select
# to get data of interest
# then store results in MyData dataframe

MyData2016 <- RawElectionData2016%>%
  filter(OFFICENAME == "United States President",
         CANDGROUP == "1") %>%
  mutate(
    Rep16 = PVTALLY1,
    Dem16 = PVTALLY2,
    Total16 = Rep16 + Dem16) %>%
  select(COUNTY, PRECINCT, OFFICENAME, Rep16, Dem16, Total16)

CountyData2016 <- MyData2016 %>% 
  select(COUNTY, Rep16, Dem16, Total16) %>% 
  group_by(COUNTY) %>% 
  summarize(across(everything(), sum)) 

# Get 2020 data

download.file(
  "https://sos-tn-gov-files.tnsosfiles.com/Nov2020PrecinctDetail.xlsx",
  "RawElectionData2020.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData2020 <- read_xlsx("RawElectionData2020.xlsx", sheet = "SOFFICEL")

# Filter, calculate, and select
# to get data of interest
# then store results in MyData dataframe

MyData2020 <- RawElectionData2020%>%
  filter(OFFICENAME == "United States President",
         CANDGROUP == "1") %>%
  mutate(
    Rep20 = PVTALLY1,
    Dem20 = PVTALLY2,
    Total20 = Rep20 + Dem20) %>%
  select(COUNTY, PRECINCT, OFFICENAME, Rep20, Dem20, Total20)

MyData2020 <- MyData2020 %>% 
  mutate(COUNTY = case_when(COUNTY == "Dekalb" ~ "DeKalb",
                            TRUE ~ COUNTY))

CountyData2020 <- MyData2020%>% 
  select(COUNTY, Rep20, Dem20, Total20) %>% 
  group_by(COUNTY) %>% 
  summarize(across(everything(), sum)) 

# Get 2024 data

CountyData2024 <- read_csv("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/CountyData2024.csv")

# Merge Data Files

AllData <- left_join(CountyData2016, CountyData2020, by = "COUNTY")
AllData <- left_join(AllData, CountyData2024, by = "COUNTY")

AllData <- AllData %>% 
  mutate(
    Rep16to20 = Rep20-Rep16,
    Dem16to20 = Dem20-Dem16,
    Rep20to24 = Rep24-Rep20,
    Dem20to24 = Dem24-Dem20,
    Rep20finish = case_when(
      Rep16to20 < 0 ~ "Loss",
      Rep16to20 > 0~ "Gain",
      TRUE ~ "No change"),
    Dem20finish = case_when(
      Dem16to20 < 0 ~ "Loss",
      Dem16to20 > 0~ "Gain",
      TRUE ~ "No change"),
    Rep24finish = case_when(
      Rep20to24 < 0 ~ "Loss",
      Rep20to24 > 0~ "Gain",
      TRUE ~ "No change"),
    Dem24finish = case_when(
      Dem20to24 < 0 ~ "Loss",
      Dem20to24 > 0~ "Gain",
      TRUE ~ "No change"))

# Get a county map

CountyMap <- get_acs(geography = "county",
                   state = "TN",
                   variables = c(Japanese_ = "DP05_0048"),
                   year = 2022,
                   survey = "acs5",
                   output = "wide",
                   geometry = TRUE)

CountyMap <- CountyMap %>%
  mutate(COUNTY = (str_remove(NAME," County, Tennessee"))) %>%
  left_join(AllData, CountyMap, by = "COUNTY") %>% 
  select(COUNTY,
         Rep16, Dem16, Total16,
         Rep20, Dem20, Total20,
         Rep24, Dem24, Total24,
         Rep16to20, Dem16to20,
         Rep20to24, Dem20to24,
         Rep20finish,Dem20finish,
         Rep24finish,Dem24finish,
         geometry)

# 2020 Map

Map16to20Rep <- mapview(
  CountyMap,
  zcol = "Rep20finish",
  col.regions = "red",
  layer.name = "Rep 2020",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Rep16", "Rep20", "Rep16to20")
  )
)

mypalette = colorRampPalette(c('blue', 'lightblue'))

Map16to20Dem <- mapview(
  CountyMap,
  zcol = "Dem20finish",
  col.regions = mypalette,
  layer.name = "Dem 2020",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Dem16", "Dem20", "Dem16to20")
  )
)

Map16to20Dem | Map16to20Rep

# 2024 Map

mypalette = colorRampPalette(c('red', 'pink'))

Map20to24Rep <- mapview(
  CountyMap,
  zcol = "Rep24finish",
  col.regions = mypalette,
  layer.name = "Rep 2024",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Rep20", "Rep24", "Rep20to24")
  )
)

mypalette = colorRampPalette(c('blue', 'lightblue'))

Map20to24Dem <- mapview(
  CountyMap,
  zcol = "Dem24finish",
  col.regions = mypalette,
  layer.name = "Dem 2024",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Dem20", "Dem24", "Dem20to24")
  )
)

Map20to24Dem | Map20to24Rep

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Voting shifts, 2020-2024